Titlebook: Intelligent and Fuzzy Systems; Digital Acceleration Cengiz Kahraman,A. Cagri Tolga,Irem Ucal Sari Conference proceedings 2022 The Editor(s)

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Equity Portfolio Optimization Using Reinforcement Learning: Emerging Market Casegly, we propose two reinforcement learning agents to solve this portfolio optimization problem under Markov decision process framework. These models do not have a prior knowledge about the stock market environment, they directly learn by trial-and-error and output policy as the portfolio weighting m

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Binge-Drinking

Miodrag Zivkovic,Ana Vesic,Nebojsa Bacanin,Ivana Strumberger,Milos Antonijevic,Luka Jovanovic,Marinae, in contemporary neurological and cognitive approaches. For example, biologist Fredrick Grinnell made an argument clarifying that ambiguity “is inherent in carrying out and reporting research” (Grinnell in Science and Engineering Ethics 5:205, .). Not only that, but he also pointed out some parame

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Ahmet Tezcan Tekin,Cem Sarıperiment show that the follow-back ratios for both of the Twitter bots are very low, which are . and .. This means that most of the Twitter users do not cooperate and only want to be followed instead of following others. Our results also exhibit the effect of different strategies on the follow-back

Mystic

Murat Levent Demircan,Kaan Aksaçd on RBMs learns fingerprint image patches in two phases. The first phase (unsupervised pre-training) involves learning an identity mapping of the input image patches. In the second phase, fine-tuning and gradient updates are performed to minimize the cost function on the training dataset. The resul

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Emre Karaşahin,Semih Utku,Okan Öztürkmenoğlud on RBMs learns fingerprint image patches in two phases. The first phase (unsupervised pre-training) involves learning an identity mapping of the input image patches. In the second phase, fine-tuning and gradient updates are performed to minimize the cost function on the training dataset. The resul

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